Users of social networking applications frequently share endorsements of one or more documents among their friends or followers in the social networking application. For example, depending on the social networking application used, a user may post a link to a webpage onto their wall for viewing by their friends, or may send a link to a webpage to their followers.
As may be appreciated, the set of documents that are endorsed by users that have a social networking relationship with the user in the social networking application (e.g., “friends”) may be useful for fulfilling queries provided by the user to a search engine. The assumption is that a user will also like documents that were endorsed or liked by their friends. Thus, when determining the documents that are most relevant to a query provided by a user, the documents that are responsive to the query that have also been endorsed by friends of the user in the social networking application may be favored over documents that are merely responsive to the query.
While considering such document endorsements enhances a user's overall search experience, incorporating the endorsements into modern search engines can be difficult. Typically, modern search engines divide the corpus of documents that make up the Internet among many index servers. When a query is received, the query is provided to each index server which then determines the most relevant documents for the query based on their individual subset of the corpus. A selection of the relevant documents from each index server is provided to, and then ranked by, a front-end server. Because of the size and dynamic nature of the social networking application, it may be difficult and unwieldy to store the data needed to incorporate document endorsements from the social networking application at each index server.